@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
/**
* @license
* Copyright 2017 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
import * as tf from '../index';
import { ALL_ENVS, describeWithFlags } from '../jasmine_util';
import { expectArraysClose } from '../test_util';
describeWithFlags('conv3dTranspose', ALL_ENVS, () => {
// Reference Python TensorFlow code
// ```python
// import numpy as np
// import tensorflow as tf
// tf.enable_eager_execution()
// x = np.array([2], dtype = np.float32).reshape(1, 1, 1, 1, 1)
// w = np.array([5, 4, 8, 7, 1, 2, 6, 3], dtype = np.float32).reshape(2, 2, 2,
// 1, 1)
// tf.nn.conv3d_transpose(x, w, output_shape=[1, 2, 2, 2, 1], padding='VALID')
// ```
it('input=2x2x2x1,d2=1,f=2,s=1,p=valid', async () => {
const origInputDepth = 1;
const origOutputDepth = 1;
const inputShape = [1, 1, 1, origOutputDepth];
const fSize = 2;
const origPad = 'valid';
const origStride = 1;
const x = tf.tensor4d([2], inputShape);
const w = tf.tensor5d([5, 4, 8, 7, 1, 2, 6, 3], [fSize, fSize, fSize, origInputDepth, origOutputDepth]);
const result = tf.conv3dTranspose(x, w, [2, 2, 2, 1], origStride, origPad);
const expected = [10, 8, 16, 14, 2, 4, 12, 6];
expect(result.shape).toEqual([2, 2, 2, 1]);
expectArraysClose(await result.data(), expected);
});
// Reference Python TensorFlow code
// ```python
// import numpy as np
// import tensorflow as tf
// tf.enable_eager_execution()
// x = np.array([2, 3], dtype = np.float32).reshape(2, 1, 1, 1, 1, 1)
// w = np.array([5, 4, 8, 7, 1, 2, 6, 3], dtype = np.float32).reshape(2,
// 2, 2, 1, 1)
// tf.nn.conv3d_transpose(x, w, output_shape=[2, 2, 2, 2, 1], padding='VALID')
// ```
it('input=2x2x2x1,d2=1,f=2,s=1,p=valid, batch=2', async () => {
const origInputDepth = 1;
const origOutputDepth = 1;
const inputShape = [2, 1, 1, 1, origOutputDepth];
const fSize = 2;
const origPad = 'valid';
const origStride = 1;
const x = tf.tensor5d([2, 3], inputShape);
const w = tf.tensor5d([5, 4, 8, 7, 1, 2, 6, 3], [fSize, fSize, fSize, origInputDepth, origOutputDepth]);
const result = tf.conv3dTranspose(x, w, [2, 2, 2, 2, 1], origStride, origPad);
const expected = [10, 8, 16, 14, 2, 4, 12, 6, 15, 12, 24, 21, 3, 6, 18, 9];
expect(result.shape).toEqual([2, 2, 2, 2, 1]);
expectArraysClose(await result.data(), expected);
});
});
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